Objective: This paper proposes a deep learning model for breast cancer detection from reconstructed images of microwave imaging scan data and aims to improve the accuracy and efficiency of breast tumor detection, which could have a significant impact on breast cancer diagnosis and treatment. Methods: Our framework consists of different convolutional neural network (CNN) architectures for feature extraction and a region-based CNN for tumor detection. We use 7 different architectures: DenseNet201, ResNet50, InceptionV3, InceptionResNetV3, MobileNetV2, NASNetMobile and NASNetLarge and compare its performance to find the best architecture out of the seven. An experimental dataset of MRI-derived breast phantoms was used. Results: NASNetLarge is the best architecture which can be used for the CNN model with accuracy of 88.41% and loss of 27.82%. Given that the model's AUC is 0.786, it can be concluded that it is suitable for use in its present form, while it could be improved upon and trained on other datasets that are comparable. Impact: One of the main causes of death in women is breast cancer, and early identification is essential for enhancing the results for patients. Due to its non-invasiveness and capacity to produce high-resolution images, microwave imaging is a potential tool for breast cancer screening. The complexity of tumors makes it difficult to adequately detect them in microwave images. The results of this research show that deep learning has a lot of potential for breast cancer detection in microwave images

翻译：目的：本文提出了一种基于微波成像扫描重建图像的深度学习模型用于乳腺癌检测，旨在提高乳腺肿瘤检测的准确性和效率，这对乳腺癌的诊断与治疗具有重要影响。方法：我们的框架包含用于特征提取的不同卷积神经网络架构以及用于肿瘤检测的区域卷积神经网络。我们采用了7种不同架构：DenseNet201、ResNet50、InceptionV3、InceptionResNetV3、MobileNetV2、NASNetMobile和NASNetLarge，并通过性能比较找出其中最优架构。实验数据集来源于MRI衍生乳腺体模。结果：NASNetLarge是最适用于卷积神经网络模型的架构，其准确率为88.41%，损失率为27.82%。鉴于模型AUC值为0.786，可判定其当前形式已具备应用可行性，同时可对该模型进行改进，并使用其他可比数据集进行训练。影响：乳腺癌是导致女性死亡的主要原因之一，早期识别对改善患者预后至关重要。微波成像因其非侵入性和生成高分辨率图像的能力，已成为乳腺癌筛查的潜在工具。然而，肿瘤的复杂性导致难以在微波图像中有效检测病灶。本研究结果表明，深度学习在微波图像乳腺癌检测领域具有巨大潜力。